Using short-term evidence to predict six-month outcomes in clinical trials of signs and symptoms in rheumatoid arthritis.
A model is presented to generate a distribution for the probability of an ACR response at six months for a new treatment for rheumatoid arthritis given evidence from a one- or three-month clinical trial. The model is based on published evidence from 11 randomized controlled trials on existing treatments. A hierarchical logistic regression model is used to find the relationship between the proportion of patients achieving ACR20 and ACR50 at one and three months and the proportion at six months. The model is assessed by Bayesian predictive P-values that demonstrate that the model fits the data well. The model can be used to predict the number of patients with an ACR response for proposed six-month clinical trials given data from clinical trials of one or three months duration.
Nixon
Richard M
RM
MRC Biostatistics Unit, Institute of Public Health, University Forvie Site, Cambridge, UK. richard.nixon@mrc-bsu.cam.ac.uk
Bansback
Nick
N
Stevens
John W
JW
Brennan
Alan
A
Madan
Jason
J
D001172:Arthritis, Rheumatoid / Q000175:diagnosis / Q000188:drug therapy*; D001499:Bayes Theorem; D001688:Biological Products / Q000627:therapeutic use; D003198:Computer Simulation; D006801:Humans; D016015:Logistic Models; D008727:Methotrexate / Q000627:therapeutic use; D016032:Randomized Controlled Trials as Topic; D012107:Research Design; D013997:Time Factors; D016896:Treatment Outcome
D001688:Biological Products; D008727:Methotrexate